| name | do-ai-agents-know-when-a-task-is-simple-toward-com |
| description | Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution - Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task a... |
| version | 1.0.0 |
| author | Junjie Yin, Xinyu Feng |
| arxiv_id | 2607.13034 |
| created | 2026-07-14T00:00:00.000Z |
| category | nlp-llm |
| tags | ["cs.AI","cs.CL","cs.SE","eess.SY"] |
| activation_keywords | ["agents","know","task","simple","toward","complexity","aware","reasoning","execution","large"] |
Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
Overview
Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy--re-reading files and dependencies they have already seen--turning a one-line edit into a small code-base audit. We argue the missing capability is task-aware execution-scope estimation: judging a task's difficulty, the information it truly needs, and the shortest reliable path before committing budget. We formalize minimum-sufficient execution and the Agent Cognitive Redundancy Ratio (ACRR), and propose E3 (Estimate, Execute, Expand): the agent estimates an initial operating point, executes a minimum viable path, and expands scope only when verification fails. On MSE-Bench--a deterministic benchmark of 121 edits in a capability-controlled simulator--E3 matches the strongest baseline's 100% success while cutting cost by 85%, tokens by 91%, and inspected files by 92%, and further beats a strong adaptive retrieval baseline by 16%; the gains survive held-out instruction wording and essentially every cost weighting. A companion real-model harness (LLM-Case) corroborates the effect on a live gpt-4o agent editing a real open-source library, with every candidate patch graded by actually running the project's real pytest suite against a measured oracle: the over-reading is milder but real, and E3 is the leanest and fastest policy at comparable task success--its one shortfall a provider rate-limit, not a wrong edit. We frame this as a controlled probe of execution redundancy, not a measurement of any deployed agent, and position task-aware execution as a step toward engineering-grounded AI (EGAI)--agents whose effort is anchored in the engineering reality of the task. We release the framework and benchmark.
Key Insights
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Implementation Approach
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Applications
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Activation Keywords
agents, know, task, simple, toward, complexity, aware, reasoning, execution, large
